今日十條 · 2026 年 9 月 25 日模型價格戰、監管落地、爬蟲改例：今日最值得企業留意的十條

我們認為價格下調不是好消息本身，而是門檻重設。真正的分野在於：同一間公司有沒有能力把省下來的成本，換成多跑的流程。省價而不改流程，等於只賺了一次折扣。We read a price cut not as the news itself but as a reset of the threshold. The real difference is whether a company can convert the savings into processes it now runs. A discount taken without changing any workflow is a one-off rebate, nothing more.

我們認為企業應該停止追逐「最強模型」，改為建立模型替換的能力。當代際成本一年可以跌四成，把業務邏輯綁死在單一模型上，就是每年自動加價一次。We think enterprises should stop chasing the strongest model and start building the ability to swap models. When generation-on-generation cost can fall 40% in a year, hard-wiring business logic to one provider is a self-imposed annual price increase.

我們認為語音是這一輪最被低估的入口。文字對話需要員工主動打字，語音可以嵌進正在做事的雙手裡。對前線工序而言，語音的滲透速度會快過桌面軟件。We think voice is the most underrated interface of this cycle. Text chat requires a worker to stop and type; voice fits inside a pair of hands that are already busy. On the frontline, voice will spread faster than any desktop tool.

我們認為企業目前最缺的不是模型，是「算得清」。多數 AI 項目失敗不是因為做不到，而是因為沒有人在立項時寫下單位成本與退出條件。當你每隔兩週就要重新評估一次模型，你需要的不是更快的評估，而是更少的評估對象。What enterprises lack right now is not models but arithmetic. Most AI projects fail not because the work was impossible but because nobody wrote down the unit cost and the exit condition when the project was approved. If you must re-evaluate models every fortnight, what you need is not faster evaluation — it is fewer candidates.

我們認為真正的瓶頸不在模型，而在政策。多數企業拿到授權之後會卡在同一個地方：內部沒有那份寫明「可以用 AI 對自家系統做什麼」的政策，於是能力買了、不敢用。這是未來十二個月最普遍的停滯點。We think the bottleneck is not the model but the policy. Most enterprises will stall at the same point after acquiring the licence: there is no internal document stating what AI may and may not do against their own systems, so the capability is bought and left unused. That is where the next twelve months get stuck.

我們認為香港與內地企業會以兩種方式被捲入：一是直接服務歐盟客戶，二是替歐盟客戶做外包。前者的風險寫在合約裡，後者的風險寫在問卷裡。而問卷來得比法規快。Hong Kong and mainland Chinese firms will be drawn in two ways: serving EU clients directly, or subcontracting for them. For the first group the risk sits in the contract; for the second it sits in a questionnaire. And the questionnaire arrives faster than the regulation does.

我們認為未來兩年「被 AI 引用」會取代「被 Google 排名」，成為內容工作的主要 KPI。而這件事有多脆弱，這一週展示得很清楚：一個你從未打開過的預設值，就可以決定你的內容會不會出現在答案裡。We think that within two years being cited by AI will displace ranking on Google as the main content KPI. How fragile that position is became clear this week: a default setting you never opened can decide whether your content appears in an answer at all.

我們認為中國市場這一輪的做法比歐美更貼近企業現實——直接把 Agent 放進既有的協作工具（釘釘、飛書）裡，而不是要員工另開一個介面。對中小企而言，最實際的訊號是：不需要先建 AI 平台，先整理好自己那份「工作上下文」。We think this round in the China market is closer to enterprise reality than the Western approach: agents are placed inside the collaboration tools staff already use — DingTalk, Feishu — rather than behind another interface. For an SME the practical signal is that you do not need to build an AI platform first; you need to organise your own working context first.

我們認為這一條是十條之中最應該貼在老闆檯頭的一條。當企業開始統計每項任務中「人與 AI 的分工比例」，管理就不再是感覺，而是可以量度的數字。給 Agent 開權限的那一刻，其實是企業在為它寫職位說明書——多數公司還沒有寫。Of the ten items today, this is the one worth pinning to the owner's desk. Once a company starts measuring the human-to-AI split on each task, management stops being a matter of impression and becomes a number. The moment you grant an agent access, you are effectively writing its job description — and most companies have not written one.

我們認為企業不需要預測泡沫，只需要分清兩件事：那些與 AI 算力掛鉤的長期承諾（例如多年雲端合約），與可按年檢視的工具支出。前者一旦簽下就難以退出，後者才是你現階段應該集中投入的地方。Enterprises do not need to call the bubble. They need to separate two things: long-term commitments tied to AI compute, such as multi-year cloud contracts, and tooling spend that can be reviewed annually. The first is hard to exit once signed; the second is where your investment belongs today.